However, this assumption seldom holds for real-world applications since noisy examples are prevalent, as shown in Figure REF . For example, an interacted user-item pair may only be attributed to the popularity bias [1]}, which could actually lead to negative user preference. These interactions can be considered as noisy-positive examples [2]}, [3]}, [4]}. Besides, a missing interaction could also be attributed to unawareness of the user because the item is not exposed to this user.
Such noisy-negative examples could have signalled potential positive user preferences.
The normal training procedure overlooks these noisy examples, leading to misunderstanding of the real user preference and sub-optimal recommendation [1]}, [4]}.
